Offshore wind farm managers and schedulers need to manage large numbers of wind turbine visits from day to day: in order to repair minor faults; conduct inspections; and perform scheduled service operations. While major operations - such as gearbox replacements - often have dedicated vessels and technicians booked in advance, the daily schedule forms a choice of which more minor maintenance activities to conduct, given the vessels and technicians available. This choice must balance weather conditions, shifts, vessel and technician capabilities, and the impacts of the various maintenance tasks on wind farm profitability. This forms a formidable optimisation challenge that today is solved "by hand" by a scheduler.

A new short-term decision support tool has been developed at ECN as part of the Daisy4Offshore joint industry project, combining expertise in offshore wind turbine accessibility, logistics and artificial intelligence. Here, the question of how to quantify the benefits of such a tool is addressed; a challenging topic given the uncertainties inherent in weather and component failure. Results are presented from applying the tool to historical operations on offshore wind farms, with the goal of objectively proving the added value of implementing such a tool in day-to-day operations.

Method

ECN Despatch is a run-on-demand tool, which takes an input set of maintenance operations, resources (vessels, technicians and spares) and constraints (weather, shift timings) and automatically creates an optimum schedule for the wind farm manager's choice of objective (e.g. maximum power production).

The tool has so far been developed as a logic engine, to be integrated into a full asset management system which handles data storage and transfer. In order to build a business case for implementation, testing the tool on historical data offers a simple approach for initial assessment of its likely value to the organisation and can be conducted stand-alone by ECN. A reliable method has now been devised to calculate the improvement in performance attributable to the decision support tool.

Results

In valuing a short-term decision support tool, it is important to consider the value of the objective beyond the weather forecast window, since benefit may come from, for instance, delaying maintenance operations to periods of future lower wind speed, to minimise energy losses. The validation methodology presented is therefore designed to consider not only the history of what was performed and the actual weather experienced, but also the information available at the time the decision was made and the adjustments required due to weather forecast inaccuracy.

During tool development, testing has been conducted on historic data from the Princess Amalia offshore wind farm. It is intended to supplement this with at least one other case study to strengthen the results.

Conclusions

Both the ECN Despatch tool and the valuation methodology have the potential to be transformative for the offshore wind industry. The quantitative results presented suggest that artificial intelligence significantly improves planning of maintenance tasks and therefore increase value on wind farms. Further, the methodology could be applied to compare operation of individual wind farms within portfolios with each other and a future industry benchmark.

Objectives

The delegates will be presented with the state of the art in short-term decision support systems for short-term maintenance planning. More importantly, they will be shown considerations for evaluating the value of such systems before taking an investment decision, developed into a full validation and valuation methodology. Finally, the delegates will be encouraged to consider what are the most effective objectives to set for a wind farm, and how short-term decision making and long-term maintenance planning relate to one another.